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检索条件"任意字段=4th Workshop on Artificial Neural Networks in Pattern Recognition"

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition, ANNPR 2024

11th IAPR TC3 workshop on Artificial Neural Networks in Patt...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition, ANNPR 2024

ISBN: (纸本)9783031716010

the proceedings contain 27 papers. the special focus in this conference is on artificial neural networks in pattern recognition. the topics include: neural Decompiling of Tracr Transformers;pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data;robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data;evaluating Support Vector Machines with Multiple Kernels by Random Search;Automatic Interpretation of 18F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks;a Hybrid Neuroevolutionary Approach to the Design of Convolutional neural networks for 2D and 3D Medical Image Segmentation;An Improved Pix2Pix GAN for Medical Image Generation;vision Transformer Features-Based Leukemia Classification;comparative Study of Deep Learning Models in Melanoma Detection;a Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification;machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease;explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task;Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via neural networks;VAeViT: Fusing Multi-views for Complete 3D Object recognition;leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos;palmprint Classification via Filter Faces and Feature Extraction;deep Multi-label Classification of Personality with Handwriting Analysis;license Plate Detection and Character recognition Using Deep Learning and Font Evaluation;experiments in Modeling Disagreement;deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers;Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming;Leveraging LSTM Embeddings for River Water Temperature Modeling;research on the Identification of Common Economic Shellfish in Jiang

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10th IAPR TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2022

10th IAPR TC3 International Workshop on Artificial Neural Ne...

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10th IAPR TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2022

ISBN: (纸本)9783031206498

the proceedings contain 16 papers. the special focus in this conference is on artificial neural networks in pattern recognition. the topics include: Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech;preface;graph Augmentation for neural networks Using Matching-Graphs;Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi;transformer-Encoder Generated Context-Aware Embeddings for Spell Correction;assessment of Pharmaceutical Patent Novelty with Siamese neural networks;white Blood Cell Classification of Porcine Blood Smear Images;medical Deepfake Detection using 3-Dimensional neural Learning;a Study on the Autonomous Detection of Impact Craters;utilization of Vision Transformer for Classification and Ranking of Video Distortions;a Novel Representation of Graphical patterns for Graph Convolution networks;minimizing Cross Intersections in Graph Drawing via Linear Splines;multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions;do Minimal Complexity Least Squares Support Vector Machines Work?.

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9th IAPR TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2020

9th IAPR TC3 International Workshop on Artificial Neural Net...

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9th IAPR TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2020

ISBN: (纸本)9783030583088

the proceedings contain 24 papers. the special focus in this conference is on artificial neural networks in pattern recognition. the topics include: Named entity disambiguation at scale;geometric attention for prediction of differential properties in 3d point clouds;preface;deep learning methods for image guidance in radiation therapy;how (not) to measure bias in face recognition networks;feature extraction: A time window analysis based on the x-ite pain database;pain intensity recognition - an analysis of short-time sequences in a real-world scenario;a deep learning approach for efficient registration of dual view mammography;deep transfer learning for texture classification in colorectal cancer histology;applications of generative adversarial networks to dermatologic imaging;typing plasmids with distributed sequence representation;kp-yolo: A modification of yolo algorithm for the keypoint-based detection of qr codes;using mask r-cnn for image-based wear classification of solid carbide milling and drilling tools;a hybrid deep learning approach for forecasting air temperature;intentional image similarity search;using cnns to optimize numerical simulations in geotechnical engineering;going for 2d or 3d? investigating various machine learning approaches for peach variety identification;a transfer learning end-to-end arabic text-to-speech (tts) deep architecture;ml-based trading models: An investigation during covid-19 pandemic crisis;innvestigate-gui - explaining neural networks through an interactive visualization tool;structured (de)composable representations trained with neural networks;long distance relationships without time travel: Boosting the performance of a sparse predictive autoencoder in sequence modeling.

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Gaussian-Mixture neural networks 11th

Gaussian-Mixture Neural Networks

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Meconcelli, Duccio Trentin, Edmondo Univ Siena DIISM Siena Italy

ISBN: (纸本)9783031716010;9783031716027

Density estimation is crucial to statistical pattern recognition, in both the supervised and unsupervised frameworks. It is still an open problem, due to its intrinsic difficulties and to the many shortcomings of statistical parametric and non-parametric techniques. artificial neural networks (ANN) have long been applied to the estimation of posterior probabilities for pattern classification (a simple supervised learning task), yet only a few attempts have been made to devise ANN-based density estimation algorithms. the paper proposes a novel algorithm for training an ANN from an unlabeled data sample of patterns randomly drawn from an underlying probability density function (PDF), say p(center dot), such that the ANN learns a robust non-parametric model of p(center dot). the algorithm leverages both the generalization capabilities of ANNs and the generality of the maximum-likelihood estimates of the parameters of Gaussian mixture models. therefore, the proposed machine is termed Gaussian-mixture neural Network (GNN). the best selling points of the GNN lie in its simplicity and effectiveness. Preliminary experimental results are reported and analyzed that involve data samples of variable size randomly drawn from PDFs of known form, either unimodal or multimodal. the GNN favorably compares with established statistical and ANN-based estimators, scoring generally higher than its competitors over a range of evaluation metrics. the code used in the experiments is made publicly available online on Github.

关键词： Density estimation Gaussian mixture model artificial neural network non-parametric estimation deep learning

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Learning Graph Matching with Graph neural networks 11th

Learning Graph Matching with Graph Neural Networks

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Dobler, Kalvin Riesen, Kaspar Univ Bern Inst Comp Sci Neubruckstr 10 CH-3012 Bern Switzerland

ISBN: (纸本)9783031716010;9783031716027

Graph matching aims at evaluating the dissimilarity of two graphs by defining a constrained correspondence between their nodes and edges. Error-tolerant graph matching, for instance, introduces the concept of a cost for penalizing structural differences in the matching. A popular method for this approach is graph edit distance, which is based on the cost of the minimal sequence of edit operations to transform a source graph into a target graph. One of the main problems of graph edit distance is the computational complexity, which is exponential in its exact form. In recent years, several approximation methods for graph edit distance have been presented which offer polynomial runtimes. In this paper, we approach the graph edit distance problem in a fundamentally different way. In particular, we propose to learn graph edit distance by means of graph neural networks. In a comprehensive experimental evaluation on six data sets, we verify that our approach not only provides comparable classification performance but also substantially reduces the runtime compared to a prominent algorithm for approximate graph edit distance computation.

关键词： Structural pattern recognition Graph Matching Graph Edit Distance Graph Representation Learning

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A Hybrid Neuroevolutionary Approach to the Design of Convolutional neural networks for 2D and 3D Medical Image Segmentation 11th

A Hybrid Neuroevolutionary Approach to the Design of Convolu...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Ramesh, Nivedha Ashfaq, Tabish Kharma, Nawwaf Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9783031716010;9783031716027

the evolution of Convolutional neural networks (CNNs) has revolutionized medical image segmentation, yet designing optimal architectures remains a challenge. In this paper, we introduce a hybrid evolutionary algorithm that advances the design of CNNs for medical image segmentation. By integrating Cartesian Genetic Programming with Simulated Annealing, our approach efficiently explores the architectural design space, yielding CNN architectures that perform robustly across several medical imaging tasks. Our model demonstrated competitive performance on the DRIVE and Medical Segmentation Decathlon datasets, achieving Dice Similarity Coefficients up to 0.828, and excelling in computational efficiency with reduced GFLOP values. Notably, our algorithm's design eliminates the need for extensive pre-training, reducing computational overhead and enhancing its suitability for resource-constrained environments.

关键词： Medical Image Segmentation Evolutionary Algorithms Computational Efficiency neural Architecture Search (NAS)

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neural Decompiling of Tracr Transformers 11th

Neural Decompiling of Tracr Transformers

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： thurnherr, Hannes Riesen, Kaspar Univ Bern Inst Comp Sci CH-3012 Bern Switzerland

ISBN: (纸本)9783031716010;9783031716027

Recently, the transformer architecture has enabled substantial progress in many areas of pattern recognition and machine learning. However, as with other neural network models, there is currently no general method available to explain their inner workings. the present paper represents a first step towards this direction. We utilize Transformer Compiler for RASP (Tracr) to generate a large dataset of pairs of transformer weights and corresponding RASP programs. Based on this dataset, we then build and train a model, with the aim of recovering the RASP code from the compiled model. We demonstrate that the simple form of Tracr compiled transformer weights is interpretable for such a decompiler model. In an empirical evaluation, our model achieves exact reproductions on more than 30% of the test objects, while the remaining 70% can generally be reproduced with only few errors. Additionally, more than 70% of the programs, produced by our model, are functionally equivalent to the ground truth, and therefore a valid decompilation of the Tracr compiled transformer weights.

关键词： Transformer Interpretability Decompiling RASP

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Pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data 11th

Pitfalls in Processing Infinite-Length Sequences with Popula...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Casoni, Michele Guidi, Tommaso Tiezzi, Matteo Betti, Alessandro Gori, Marco Melacci, Stefano Univ Siena DIISM I-52100 Siena Italy IMT Scuola Alti Studi I-55100 Lucca Italy

ISBN: (纸本)9783031716010;9783031716027

One of the enduring challenges for the Machine Learning community is developing models that can process and learn from very long data sequences. Transformer-based models and Recurrent neural networks (RNNs) have excelled in processing long sequences, yet face challenges in transitioning to processing infinite-length sequences online, a crucial step in mimicking human learning over continuous data streams. While Transformer models handle large context windows efficiently, they suffer from quadratic computational costs, motivating research into alternative attention mechanisms. Conversely, RNNs, particularly Deep State-Space Models (SSMs), have shown promise in long sequence tasks, outperforming Transformers in certain benchmarks. However, current approaches are limited to finite-length sequences, which are pre-buffered and randomly shuffled to cope with stochastic gradient descent. this paper addresses the fundamental gap in transitioning from offline-processing of a dataset of sequences to online-processing of possibly infinite-length sequences, a scenario often neglected in existing research. Empirical evidence is presented, demonstrating the performance and limits of existing models. We highlight the challenges and opportunities in learning from a continuous data stream, paving the way for future research in this area.

关键词： Recurrent neural networks Transformers Long Sequences Online Learning Learning over Time

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Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions 11th

Generative Plant Growth Simulation from Sequence-Informed En...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Debbagh, Mohamed Liu, Yixue Zheng, Zhouzhou Jiang, Xintong Sun, Shangpeng Lefsrud, Mark McGill Univ Montreal PQ H3A 0G4 Canada Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9783031716010;9783031716027

A plant growth simulation can be characterized as a reconstructed visual representation of a plant or plant system. the phenotypic characteristics and plant structures are controlled by the scene environment and other contextual attributes. Considering the temporal dependencies and compounding effects of various factors on growth trajectories, we formulate a probabilistic approach to the simulation task by solving a frame synthesis and pattern recognition problem. We introduce a sequence-informed plant growth simulation framework (SI-PGS) that employs a conditional generative model to implicitly learn a distribution of possible plant representations within a dynamic scene from a fusion of low-dimensional temporal sensor and context data. Methods such as controlled latent sampling and recurrent output connections are used to improve coherence in the plant structures between frames of prediction. In this work, we demonstrate that SI-PGS is able to capture temporal dependencies and continuously generate realistic frames of plant growth.

关键词： Plant Growth Simulation Continuous Frame Synthesis Conditional Generative Model

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Assessment of Pharmaceutical Patent Novelty with Siamese neural networks 10th

Assessment of Pharmaceutical Patent Novelty with Siamese Neu...

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10th IAPR TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： El-Shimy, Heba Zantout, Hind Hassen, Hani Ragab Heriot Watt Univ Dubai U Arab Emirates

ISBN: (纸本)9783031206498;9783031206504

Patents in the pharmaceutical field fulfil an important role as they contain details of the final product that is the culmination of years of research and possibly millions of dollars of investment. It is crucial that both patent producers and consumers are able to assess the novelty of such patents and perform basic processing on them. In this work, we review approaches in the literature in patent analysis and novelty assessment that range from basic digitisation to deep learning-based approaches including natural language processing, image processing and chemical structure extraction. We propose a system that automates the process of patent novelty assessment using Siamese neural networks for similarity detection. Our system showed promising results and has a potential to improve upon the current patent analysis methods, specifically in the pharmaceutical field, by not just focusing on the task from a Natural Language Processing perspective, but also, adding image analysis and adaptations for chemical structure extraction.

关键词： Document analysis Siamese neural networks CNN LSTM Optical character recognition Pharmaceutical patents Chemical structure extraction

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